packages <- c(
"broom", "corrplot", "dplyr", "emmeans","ggplot2", "ggpubr", "ggrepel","fsmb", "gridExtra", "grid", "glmnet", "gbm", "ISLR", "leaps", "MASS","nnet", "pROC",
"readxl", "rlang", "randomForest", "tidyverse","tidyr", "tree", "writexl"
)
# Packages will only be installed if not already!
for (pkg in packages) {
if (!requireNamespace(pkg, quietly = TRUE)) {
#install.packages(pkg)
}
}
library(broom)
library(corrplot)
library(dplyr)
library(emmeans)
library(ggplot2)
library(ggpubr)
library(ggrepel)
library(fmsb) #radar plot
library(gridExtra)
library(grid)
library(glmnet)
library(gbm)
library(ISLR)
library(leaps)
library(MASS)
library(nnet)
library(pROC)
library(readxl)
library(rlang)
library(randomForest)
library(tidyverse)
library(tidyr)
library(tree)
library(writexl)SIBDS Chagas Disease Project
Chagas Disease Biomarker Analysis
During my internship at the University of Texas Medical Branch (UTMB) in the Department of Data Science and Biostatistics, I worked under the guidance of Dr. Garg and Dr. Choudhuri, along with my partner Summer Johnson. My role was to analyze biomedical data provided by our Principal Investigator, derive insights, and present the findings.
The research aimed to identify diagnostic and prognostic biomarkers for Chagas Disease — a cardiovascular illness caused by the parasite T. cruzi. The disease is widespread in Latin America and is responsible for an estimated 3 million deaths annually. Our long-term goal was to discover blood-based biomarkers that could inform vaccine development and enable more accessible diagnostic tests.
Our data set contained:
- 42 subjects total
- 12 Normal Healthy Subjects (controls)
- 30 with Chagas Disease
- 15 symptomatic
- 15 asymptomatic
- Biomarkers recorded:
- 6 protein parameters, measured in both plasma and serum (12 total protein biomarkers)
- 5 mitochondrial DNA (mtDNA) types
With my research partner Summer Johnson, we performed logistic regression modeling for each biomarker:
- Diagnostic biomarkers: distinguish Chagas patients from Normal Healthy Subjects.
- Prognostic biomarkers: distinguish symptomatic from asymptomatic patients.
Several biomarkers demonstrated strong diagnostic and prognostic potential. Below are the final results (plots + tables).
Below are step by step processes used for this research.
Install & Load Packages
Clean Data
Adjusted the data set to create a column for each protein & type. 8 protein parameters, 2 types. 16 total. Then create numeric values for categorical data NHS, SYM, ASM ->1s or 0s.
data <- read_xlsx("~/R Notes/proteinparam.xlsx")
#Create columns for each protein parameter and test, new column ordinal chagas present
data <- data %>%
pivot_wider(names_from = Moleculae, values_from = c(Serum, Plasma),
names_glue = "{Moleculae}_{.value}") %>%
mutate(
chag = case_when(
Symptom == "NHS" ~ 0,
Symptom == "ASYM" | Symptom == "SYM" ~ 1,
TRUE ~ NA_real_
),
sym = case_when(
Symptom == "ASYM" ~ 0,
Symptom == "SYM" ~ 1,
TRUE ~ NA_real_
)
)Exploratory Data Analysis
Create box plots to show variability of protein parameter expression.
# Create a list marker_labels full of each biomarker
marker_labels <- c(
"Copeptin_Serum" = "Copeptin (S)",
"Copeptin_Plasma" = "Copeptin (P)",
"HnRNPA1_Serum" = "HnRNPA1 (S)",
"HnRNPA1_Plasma" = "HnRNPA1 (P)",
"Endostatin_Serum" = "Endostatin (S)",
"Endostatin_Plasma" = "Endostatin (P)",
"etOhDG_Serum" = "8-OhDG (S)",
"etOhDG_Plasma" = "8-OhDG (P)",
"Myostatin_Serum" = "Myostatin (S)",
"Myostatin_Plasma" = "Myostatin (P)",
"PARP1_Serum" = "PARP1 (S)",
"PARP1_Plasma" = "PARP1 (P)"
)
#Chagas/ No Chagas boxplot
df_long <- data %>%
pivot_longer(cols = ends_with("_Serum") | ends_with("_Plasma"),
names_to = "marker",
values_to = "value")
chag_labels <- c("0" = "No Chagas", "1" = "Has Chagas")
ggplot(data = df_long,
aes(x = factor(chag), y = value, fill = factor(chag))) +
geom_boxplot() +
facet_wrap(~ marker,
scales = "free",
labeller = labeller(marker = marker_labels)) +
scale_fill_manual(
name = "Chagas Status",
values = c("0" = "#FF61CC", "1" = "#00A9FF"),
labels = chag_labels
) +
labs(
x = "Chagas Status",
y = "Protein Level",
fill = "Symptom Status"
) 
#Sym/Asym boxplot
df_long2 <- data %>%
pivot_longer(cols = ends_with("_Serum") | ends_with("_Plasma"),
names_to = "marker",
values_to = "value") %>%
filter(sym == 0 | sym == 1)
sym_labels <- c("0" = "Asymptomatic", "1" = "Symptomatic")
ggplot(df_long2,
aes(x = factor(sym), y = value, fill = factor(sym))) +
geom_boxplot() +
facet_wrap(~ marker,
scales = "free",
labeller = labeller(marker = marker_labels)) +
scale_fill_manual(
name = "Symptom Status",
values = c("0" = "#1ABC9C", "1" = "red"),
labels = sym_labels
) +
labs(
x = "Chagas Symptom Status",
y = "Protein Level",
fill = "Symptom Status"
) 
#Group averages
avg_data <- data %>%
group_by(Symptom) %>%
summarise(across(matches("(_Serum|_Plasma)$"), mean, na.rm = TRUE)) %>%
as.data.frame()
log_data <- avg_data %>%
mutate(across(where(is.numeric), log))
# Set rownames from Symptom and remove that column
rownames(log_data) <- log_data$Symptom
log_data$Symptom <- NULL
# Set min and max values explicitly
min_val <- 0 # your observed raw minimum
max_val <- 8 # your observed raw maximum
custom_labels <- c("Copeptin (S)", "HnRNPA1 (S)", "Endostatin (S)", "Myostatin (S)",
"8-OHdG (S)", "PARP1 (S)", "Copeptin (P)", "HnRNPA1 (P)", "Endostatin (P)", "Myostatin (P)",
"8-OHdG (P)", "PARP1 (P)")
# Create df with required min and max rows
df_radar <- log_data
colnames(df_radar) <- custom_labels
df_radar <- rbind(rep(max_val, ncol(df_radar)),
rep(min_val, ncol(df_radar)),
df_radar)
# Define your custom sizes
axis_label_size <- 0.8 # Size of the axis label text
variable_label_size <- 1.2 # Size of the variable labels (vlcex)
title_size <- 1.5 # Size of the title
radarchart(df_radar,
axistype = 1,
caxislabels = round(seq(min_val, max_val, length.out = 5), 2),
plwd = 2,
cglcol = "grey",
cglty = 1,
axislabcol = "black",
calcex = axis_label_size * 1.2, # scale axis labels
vlcex = variable_label_size * 1.2, # scale variable names
title = "Log Averages of Protein Parameters by Symptom"
)
print(avg_data) Symptom Copeptin_Serum HnRNPA1_Serum Endostatin_Serum Myostatin_Serum
1 ASYM 961.3104 6.997077 1006.0905 57.74151
2 NHS 291.0829 1.647571 422.1989 56.70189
3 SYM 1840.1470 5.365387 1841.0096 13.58880
etOhDG_Serum PARP1_Serum Copeptin_Plasma HnRNPA1_Plasma Endostatin_Plasma
1 21.600002 196.7232 999.8742 6.152438 836.1117
2 9.520813 21.4447 255.6459 1.889371 363.0873
3 28.515085 221.4311 2085.7043 4.514841 1580.6557
Myostatin_Plasma etOhDG_Plasma PARP1_Plasma
1 51.32528 23.762719 205.4989
2 47.69188 9.742266 32.5762
3 12.54273 27.658256 209.2229Analysis
Perform Logistic Regression to see if each Protein Parameter can classify whether a patient has or does not have Chagas Disease.
list_roc_log_chag <- list()
list_cve_log_chag <- list()
# Create a summary table
summary_results <- data.frame(
ProteinParameter = character(),
AUC = numeric(),
CVE = numeric(),
stringsAsFactors = FALSE
)
protein_list <- c("Copeptin", "HnRNPA1", "Endostatin",
"Myostatin", "etOhDG", "PARP1")
sample_types <- c("Serum", "Plasma")
for (protein_name in protein_list) {
for (sample_type in sample_types) {
var_name <- paste0(protein_name, "_", sample_type)
# Logistic regression
formula <- as.formula(paste("chag ~", var_name))
log.reg <- glm(formula, data = data, family = "binomial")
#print(summary(log.reg))
# Odds ratios and confidence intervals
#cat("\nOdds Ratios:\n")
#print(exp(coef(log.reg)))
#cat("\n95% CI for Odds Ratios:\n")
#print(exp(confint(log.reg)))
# Predict probabilities
pred.prob <- predict(log.reg, type = "response")
# ROC and AUC
roc_log <- roc(data$chag, pred.prob)
roc_plot <- ggroc(roc_log) +
ggtitle(paste(var_name, "ROC")) +
theme_minimal(base_size = 9)
list_roc_log_chag[[var_name]] <- roc_plot
auc_val <- auc(roc_log)
# CV error as ggplot
set.seed(67)
x <- as.matrix(cbind(var = data[[var_name]], dummy = rnorm(nrow(data), 0, 1e-6)))
y <- as.factor(data$chag)
cvfit <- cv.glmnet(x, y, family = "binomial", alpha = 0)
cv_df <- data.frame(lambda = log(cvfit$lambda), cvm = cvfit$cvm, cvsd = cvfit$cvsd)
# Extract CVE (at lambda.min)
cve_val <- min(cvfit$cvm)
cve_plot <- ggplot(cv_df, aes(x = lambda, y = cvm)) +
geom_line(color = "blue") +
geom_ribbon(aes(ymin = cvm - cvsd, ymax = cvm + cvsd), alpha = 0.2, fill = "blue") +
geom_vline(xintercept = log(cvfit$lambda.min), linetype = "dashed", color = "red") +
labs(title = paste(var_name, "CVE"), x = "log(Lambda)", y = "Mean CV Error") +
theme_minimal(base_size = 8)
list_cve_log_chag[[var_name]] <- cve_plot
# Save to summary table
summary_results <- rbind(summary_results, data.frame(
ProteinParameter = var_name,
AUC = auc_val,
CVE = cve_val
))
}
}
grid.arrange(grobs = list_roc_log_chag,
top = textGrob("Protein Params Predict Chagas vs w/o",
just = "top",
gp = gpar(fontsize = 16, fontface = "bold")),
ncol = 4)
grid.arrange(grobs = list_cve_log_chag,
top = textGrob("Cross-Validation Error Chagas vs w/p",
just = "top",
gp = gpar(fontsize = 16, fontface = "bold")),
ncol = 4)
print(summary_results) ProteinParameter AUC CVE
1 Copeptin_Serum 1.0000000 0.4258889
2 Copeptin_Plasma 1.0000000 0.4209512
3 HnRNPA1_Serum 0.9888889 0.5012373
4 HnRNPA1_Plasma 0.9847222 0.5720976
5 Endostatin_Serum 0.9222222 0.7513106
6 Endostatin_Plasma 0.9513889 0.6884595
7 Myostatin_Serum 0.7500000 1.1589647
8 Myostatin_Plasma 0.7013889 1.2179962
9 etOhDG_Serum 0.9972222 0.3600053
10 etOhDG_Plasma 1.0000000 0.3096185
11 PARP1_Serum 1.0000000 0.3324260
12 PARP1_Plasma 1.0000000 0.3489596Perform Logistic Regression to see if each Protein Parameter can classify whether a Chagas Patient is Symptomatic or Asymptomatic.
list_roc_log_asymsym <- list()
list_cve_log_asymsym <- list()
# Create a summary table
summary_results <- data.frame(
ProteinParameter = character(),
AUC = numeric(),
CVE = numeric(),
stringsAsFactors = FALSE
)
protein_list <- c("Copeptin", "HnRNPA1", "Endostatin",
"Myostatin", "etOhDG", "PARP1")
sample_types <- c("Serum", "Plasma")
# Remove rows with NA in outcome or predictor
clean_data <- data[!is.na(data[[var_name]]) & !is.na(data$sym), ]
for (protein_name in protein_list) {
for (sample_type in sample_types) {
var_name <- paste0(protein_name, "_", sample_type)
# Logistic regression
formula <- as.formula(paste("sym ~", var_name))
log.reg <- glm(formula, data = clean_data, family = "binomial")
#print(summary(log.reg))
# Odds ratios and confidence intervals
#cat("\nOdds Ratios:\n")
#print(exp(coef(log.reg)))
#cat("\n95% CI for Odds Ratios:\n")
#print(exp(confint(log.reg)))
# Predict probabilities
pred.prob <- predict(log.reg, type = "response")
# ROC and AUC
roc_log <- roc(clean_data$sym, pred.prob)
roc_plot <- ggroc(roc_log) +
ggtitle(paste(var_name, "ROC")) +
theme_minimal(base_size = 9)
list_roc_log_asymsym[[var_name]] <- roc_plot
auc_val <- auc(roc_log)
# CV error as ggplot
set.seed(67)
x <- as.matrix(cbind(var = clean_data[[var_name]], dummy = rnorm(nrow(clean_data), 0, 1e-6)))
y <- as.factor(clean_data$sym)
cvfit <- cv.glmnet(x, y, family = "binomial", alpha = 0)
cv_df <- data.frame(lambda = log(cvfit$lambda), cvm = cvfit$cvm, cvsd = cvfit$cvsd)
# Extract CVE (at lambda.min)
cve_val <- min(cvfit$cvm)
cve_plot <- ggplot(cv_df, aes(x = lambda, y = cvm)) +
geom_line(color = "blue") +
geom_ribbon(aes(ymin = cvm - cvsd, ymax = cvm + cvsd), alpha = 0.2, fill = "blue") +
geom_vline(xintercept = log(cvfit$lambda.min), linetype = "dashed", color = "red") +
labs(title = paste(var_name, "CVE"), x = "log(Lambda)", y = "Mean CV Error") +
theme_minimal(base_size = 8)
list_cve_log_asymsym[[var_name]] <- cve_plot
# Save to summary table
summary_results <- rbind(summary_results, data.frame(
ProteinParameter = var_name,
AUC = auc_val,
CVE = cve_val
))
}
}
grid.arrange(grobs = list_roc_log_asymsym,
top = textGrob("mtDNA Types Predict Sym vs Asym",
just = "top",
gp = gpar(fontsize = 16, fontface = "bold")),
ncol = 4)
grid.arrange(grobs = list_cve_log_asymsym,
top = textGrob("Cross-Validation Error Sym vs Asym",
just = "top",
gp = gpar(fontsize = 16, fontface = "bold")),
ncol = 4)
print(summary_results) ProteinParameter AUC CVE
1 Copeptin_Serum 0.9822222 0.5935176
2 Copeptin_Plasma 1.0000000 0.4061874
3 HnRNPA1_Serum 0.7066667 1.4303023
4 HnRNPA1_Plasma 0.6133333 1.4589186
5 Endostatin_Serum 0.8911111 1.0360013
6 Endostatin_Plasma 0.9155556 0.9151671
7 Myostatin_Serum 1.0000000 0.5204222
8 Myostatin_Plasma 0.9955556 0.5867828
9 etOhDG_Serum 0.9911111 0.5646229
10 etOhDG_Plasma 0.8355556 1.2158489
11 PARP1_Serum 0.6044444 1.5383728
12 PARP1_Plasma 0.5244444 1.5389460Data Visualization - Graphing Results
#Create a tidy dataframe of AUC results
diagnosis_auc <- tribble(
~Biomarker, ~Sample, ~AUC,
"Copeptin", "Serum", 1,
"Copeptin", "Plasma", 1,
"HnRNPA1", "Serum", 0.9888889,
"HnRNPA1", "Plasma", 0.9847222,
"Endostatin", "Serum", 0.9222222,
"Endostatin", "Plasma", 0.9513889,
"Myostatin", "Serum", 0.75,
"Myostatin", "Plasma", 0.7013889,
"8-OhDG", "Serum", 0.9972222,
"8-OhDG", "Plasma", 1,
"PARP1", "Serum", 1,
"PARP1", "Plasma", 1
)
prognosis_auc <- tribble(
~Biomarker, ~Sample, ~AUC,
"Copeptin", "Serum", 0.9822222,
"Copeptin", "Plasma", 1,
"HnRNPA1", "Serum", 0.7066667,
"HnRNPA1", "Plasma", 0.6133333,
"Endostatin", "Serum", 0.8911111,
"Endostatin", "Plasma", 0.9155556,
"Myostatin", "Serum", 1,
"Myostatin", "Plasma", 0.9955556,
"8-OhDG", "Serum", 0.9911111,
"8-OhDG", "Plasma", 0.8355556,
"PARP1", "Serum", 0.6044444,
"PARP1", "Plasma", 0.5244444
)
#Categorize AUC levels
auc_category <- function(x) {
case_when(
x >= 0.9 ~ "Excellent (≥ 0.9)",
x >= 0.8 ~ "Good (0.8–0.9)",
TRUE ~ "Moderate/Low (< 0.8)"
)
}
#Category columns
diagnosis_auc <- diagnosis_auc %>%
mutate(Category = auc_category(AUC),
Group = "With vs W/o Chagas")
prognosis_auc <- prognosis_auc %>%
mutate(Category = auc_category(AUC),
Group = "Asym vs Sym")
#Combine
combined_auc <- bind_rows(diagnosis_auc, prognosis_auc)
# Classification vector
classification_df <- tribble(
~Biomarker, ~Classification,
"Copeptin", "Both",
"HnRNPA1", "Diagnostic",
"Endostatin", "Both",
"Myostatin", "Prognostic",
"8-OhDG", "Both",
"PARP1", "Diagnostic"
)
# Merge classification into AUC data
combined_auc_labeled <- combined_auc %>%
left_join(classification_df, by = "Biomarker")
# Plot with biomarker role
ggplot(combined_auc_labeled, aes(x = reorder(paste(Biomarker, Sample, sep = " "), AUC),
y = AUC, fill = Classification)) +
geom_col() +
coord_flip() +
facet_wrap(~Group, scales = "free_y") +
scale_fill_manual(values = c("Diagnostic" = "#2d0594FF",
"Prognostic" = "#9512A1FF",
"Both" = "#DD5E66FF")) +
labs(title = "AUC Values for Biomarkers in Chagas Disease",
subtitle = "Biomarkers classified as Diagnostic, Prognostic, or Both",
x = "Biomarker",
y = "AUC",
fill = "Biomarker Role") +
theme_minimal() 
Import Data
mdata <- read_xlsx("~/R Notes/MitochondrialDNA.xlsx")
# "disease" column for asym,sym (1) and NHS (0)
mdata <- mdata %>%
mutate(disease = case_when(
Symptom == "NHS" ~ 0,
Symptom == "Asymptomatic C" ~ 1,
Symptom == "Symptomatic C" ~ 1
)
)
# "sym" column for asym (0) and sym (1)
mdata <- mdata %>%
mutate(sym = case_when(
Symptom == "Asymptomatic C" ~ 0,
Symptom == "Symptomatic C" ~ 1
))
# These lists will be used for loops :D
list_DNA_Types <- c("mtND1", "mtND5", "mtATP6", "mtCOII", "mtCytB")
list_Group_Types <- c("NHS", "Asymptomatic C", "Symptomatic C")Perform Logistic Regression to see if each Mitochondrial DNA Type can classify whether a patient has or does not have Chagas Disease.
# Empty lists to store ggplots
plot_list <- list()
cve_plot_list <- list()
# Create a summary table
summary_results <- data.frame(
ProteinParameter = character(),
AUC = numeric(),
CVE = numeric(),
stringsAsFactors = FALSE
)
for (DNA_Type in list_DNA_Types) {
# --- ROC plots ---
formula_text <- paste("disease ~", DNA_Type)
mit_model <- glm(as.formula(formula_text), data = mdata, family = binomial)
prob <- predict(mit_model, type = "response")
roc_obj <- roc(mdata$disease, prob)
roc_plot <- ggroc(roc_obj) +
ggtitle(paste(DNA_Type, "ROC")) +
theme_minimal()
auc_value <- auc(roc_obj)
#print(paste0(DNA_Type, " AUC: ", auc_value))
plot_list[[DNA_Type]] <- roc_plot
# --- CV error plots ---
set.seed(67)
x <- as.matrix(cbind(
var = mdata[[DNA_Type]],
dummy = rnorm(nrow(mdata), 0, 1e-6) # tiny noise column
))
y <- as.factor(mdata$disease)
cvfit <- cv.glmnet(x, y, family = "binomial", alpha = 0)
cv_df <- data.frame(
lambda = log(cvfit$lambda),
cvm = cvfit$cvm,
cvsd = cvfit$cvsd
)
# Extract CVE (at lambda.min)
cve_value <- min(cvfit$cvm)
cve_plot <- ggplot(cv_df, aes(x = lambda, y = cvm)) +
geom_line(color = "blue") +
geom_ribbon(aes(ymin = cvm - cvsd, ymax = cvm + cvsd),
alpha = 0.2, fill = "blue") +
geom_vline(xintercept = log(cvfit$lambda.min),
linetype = "dashed", color = "red") +
labs(title = paste(DNA_Type, "CV Error"),
x = "log(Lambda)", y = "Mean CV Error") +
theme_minimal(base_size = 12)
cve_plot_list[[DNA_Type]] <- cve_plot
# Save to summary table
summary_results <- rbind(summary_results, data.frame(
biomarker = DNA_Type,
AUC = auc_value,
CVE = cve_value
))
}
# Arrange plots
grid.arrange(grobs = cve_plot_list,
top = textGrob("Cross-Validation Error by mtDNA Type",
gp = gpar(fontsize = 16, fontface = "bold")),
ncol = 4)
grid.arrange(grobs = plot_list,
top = textGrob("mtDNA Types Predict Diseased vs NHS",
gp = gpar(fontsize = 16, fontface = "bold")),
ncol = 4)
print(summary_results) biomarker AUC CVE
1 mtND1 0.8361111 0.9922597
2 mtND5 0.5833333 1.1596412
3 mtATP6 0.8138889 1.1010238
4 mtCOII 0.4416667 1.1996443
5 mtCytB 0.5444444 1.1934739Perform Logistic Regression to see if each Mitochondrial DNA Type can classify whether a patient is Symptomatic or Asymptomatic.
# Empty lists to store ggplots
plot_list <- list()
cve_plot_list <- list()
# Create a summary table
summary_results <- data.frame(
ProteinParameter = character(),
AUC = numeric(),
CVE = numeric(),
stringsAsFactors = FALSE
)
# Remove rows with NAs in the 'sym' column
asmdata <- mdata %>%
filter(!is.na(sym))
for (DNA_Type in list_DNA_Types) {
# Design matrix (with intercept)
x <- model.matrix(as.formula(paste("~", DNA_Type)), data = asmdata)
y <- as.factor(asmdata$sym)
# Cross-validated glmnet model
cvfit <- cv.glmnet(x, y, family = "binomial", alpha = 0)
# --- ROC plots (use cv.glmnet fit) ---
prob <- predict(cvfit, newx = x, s = "lambda.min", type = "response")
roc_obj <- roc(asmdata$sym, as.numeric(prob))
roc_plot <- ggroc(roc_obj) +
ggtitle(paste(DNA_Type, "ROC")) +
theme_minimal()
auc_value <- auc(roc_obj)
#print(paste0(DNA_Type, " AUC: ", auc_value))
plot_list[[DNA_Type]] <- roc_plot
# --- CV error plots ---
set.seed(67)
cv_df <- data.frame(
lambda = log(cvfit$lambda),
cvm = cvfit$cvm,
cvsd = cvfit$cvsd
)
# Extract CVE (at lambda.min)
cve_value <- min(cvfit$cvm)
cve_plot <- ggplot(cv_df, aes(x = lambda, y = cvm)) +
geom_line(color = "blue") +
geom_ribbon(aes(ymin = cvm - cvsd, ymax = cvm + cvsd),
alpha = 0.2, fill = "blue") +
geom_vline(xintercept = log(cvfit$lambda.min),
linetype = "dashed", color = "red") +
labs(title = paste(DNA_Type, "CV Error"),
x = "log(Lambda)", y = "Mean CV Error") +
theme_minimal(base_size = 12)
cve_plot_list[[DNA_Type]] <- cve_plot
# Save to summary table
summary_results <- rbind(summary_results, data.frame(
biomarker = DNA_Type,
AUC = auc_value,
CVE = cve_value
))
}
# Arrange plots
grid.arrange(grobs = cve_plot_list,
top = textGrob("Cross-Validation Error by mtDNA Type",
gp = gpar(fontsize = 16, fontface = "bold")),
ncol = 4)
grid.arrange(grobs = plot_list,
top = textGrob("DNA Types Predict Asymptomatic vs Symptomatic",
gp = gpar(fontsize = 16, fontface = "bold")),
ncol = 4)
print(summary_results) biomarker AUC CVE
1 mtND1 0.9911111 0.6311849
2 mtND5 1.0000000 0.9183945
3 mtATP6 1.0000000 0.8741642
4 mtCOII 0.9244444 0.9374986
5 mtCytB 0.9422222 0.9852878Visualize Results
# AUC dataframe
auc_mtdata <- data.frame(
Gene = c("MtND1", "MtND5", "MtATP6", "MtcCOII", "MtCytB"),
Control_vs_Disease = c(0.836, 0.583, 0.813, 0.441, 0.544),
Sym_vs_Asym = c(0.991, 1, 1, 0.924, 0.942)
)
# Pivot to long format
data_long <- pivot_longer(auc_mtdata,
cols = -Gene,
names_to = "Comparison",
values_to = "AUC")
# Classify role
gene_classification <- auc_mtdata %>%
mutate(
Role = case_when(
Control_vs_Disease >= 0.8 & Sym_vs_Asym >= 0.8 ~ "Diagnostic &\n Prognostic",
Control_vs_Disease >= 0.8 ~ "Diagnostic",
Sym_vs_Asym >= 0.8 ~ "Prognostic",
TRUE ~ "Neither"
)
)
# Reshape for ggplot
data_long <- pivot_longer(gene_classification,
cols = c(Control_vs_Disease, Sym_vs_Asym),
names_to = "Comparison",
values_to = "AUC") %>%
mutate(
ComparisonLabel = case_when(
Comparison == "Control_vs_Disease" ~ "With vs W/o Chagas",
Comparison == "Sym_vs_Asym" ~ "Asym vs Sym"
)
)
# Base plot
plot_main <- ggplot(data_long, aes(x = AUC, y = reorder(Gene, AUC), fill = Role)) +
geom_col() +
facet_wrap(~ComparisonLabel, scales = "free_y") +
geom_vline(xintercept = 0.8, linetype = "dashed", color = "black", linewidth = 0.5) +
scale_fill_manual(
values = c(
# Pink = "#DD5E66",
# Blue = "#2d0594",
# Purple = "#9512A1",
"Diagnostic &\n Prognostic" = "#9512A1",
"Diagnostic" = "#DD5E66",
"Prognostic" = "#2d0594",
"Neither" = "gray70"
)
) +
labs(
title = "AUC Values for MtDNA Types in Chagas Disease",
x = "AUC", y = "Mitochondrial DNA Type"
) +
theme_minimal() +
theme(legend.title = element_text(face = "bold")) +
labs(fill = "Biomarker Role")
plot(plot_main)